摘要 :
The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. Existing benefit-risk methods depend only on the frequencies of mutually exclu...
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The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. Existing benefit-risk methods depend only on the frequencies of mutually exclusive and exhaustive categories in which the subjects fall, and the responses of individuals are allowed to belong to any of the other categories during their postwithdrawal visits. In this article we introduce a semiparametric Bayesian Markov model (SBMM) that treats the withdrawal category as an absorbing state and analyzes subject-level data for multiple visits, accounting for any within-patient dependencies in the response profiles. A log-odds ratio model is used to model the subject-level effects by assuming a ratio of transition probabilities with respect to a "reference" category. A Dirichlet process is used as a semiparametric model for the subject-level effects to flexibly capture the underlying distributions of the personalized response profiles without making strong parametric assumptions. This also allows the borrowing of strength between the patients and achieves dimension reduction by allocating similar response profiles patterns into an unknown number of latent clusters. We analyze a motivating clinical trial dataset to assess the personalized benefit-risks in each arm and evaluate the aggregated benefits and risks associated with the drug Exalgo.
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摘要 :
In 2012 and 2016, the first two strategic science-business media models were published (SBBMM 1.0 and 2.0). Since that time, there have been significant changes both to the media landscape and to the usage and capability of online...
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In 2012 and 2016, the first two strategic science-business media models were published (SBBMM 1.0 and 2.0). Since that time, there have been significant changes both to the media landscape and to the usage and capability of online and social media platforms. This paper seeks to describe the current bioindustry-relevant media landscape, to introduce a new media model, the Strategic Bioenterprise Media Model 2020 (SBMM 2020), which reflects this new landscape, and to present a mainstream submodel to support the latest opportunity for biotechnology media coverage: Mainstream Media. Examples are drawn from media coverage following the FDA approvals of Zulresso from Sage Therapeutics, Aimovig from Novartis and Amgen, and AquAdvantage salmon from AquAdvantage Technologies. The overall goal of this paper is to equip bioenterprise professionals with an understanding of media dynamics and the strategic potential it brings, ultimately contributing to bioenterprise success.
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